1. Technical Field
An embodiment of the present invention relates generally to a technology for extracting an object of interest from an image and, more particularly, to a technology for extracting an object of interest from an image using a saliency map, a trimap, and an alpha map.
2. Description of the Related Art
Technology for automatically segmenting and extracting an object inside image content is an issue that is considered to be of great importance in the field of computer vision. Technology for automatically segmenting and extracting an object inside image content corresponds to a core module for performing application services, such as object-of-interest image segmentation, object recognition, object annotation, adaptive image compression, image retrieval, image-based content creation (e.g., image synthesis and non-realistic rendering). However, a corresponding problem is an intrinsically ill-posed problem, and requires additional constraint conditions in order to obtain a stable solution (i.e., a precisely segmented and extracted object-of-interest region). The additional constraint conditions are provided in the form of the input of preliminary information by a user (e.g., the user labeling of pixels having clear information) or in the form of the random assumption of a color model (e.g., a linear color model, a Gaussian mixture model, or the like).
In particular, an object extraction method for the object recognition, object annotation and image search of the aforementioned application services tends to chiefly depend on a conventional method of performing detection by scanning rectangular sliding windows. This method presents a difficulty in providing accurate spatial support for an object of interest (i.e., a subregion including a target object). That is, in order to provide accurate spatial support for an object and guarantee the high performance of related application services, an object of interest, not an approximate region of interest, needs to be precisely extracted.
Furthermore, there is a need for an improved method for automation because it is practically impossible for a user to set each constraint in many application services using a variety of large-scale images.
Most image segmentation and abstraction technologies correspond to region grouping based on an image color distribution. These image segmentation and abstraction technologies have a limitation in that they have different region group labels (i.e., the indices of subregions distinguished by exclusive labels) even within a specific object of interest, and have a problem in that they obtain coarse segmentation results attributable to the inaccuracy of an edge between different regions that are spatially close to each other.
Furthermore, in accurate image object extraction, an object is precisely extracted by probabilistically calculating a transparency (opacity or alpha matte) value between a specific object region and other regions using a method called image matting. However, this has a limitation in that preliminary information (i.e., a preliminary information map in which foreground and background labels are specified for some pixels inside an image in a trimap or scramble form) is required simultaneously with the input of an original image (chiefly by a user).
Korean Patent No. 1384627 discloses a technology for rapidly segmenting an object, including a flower, in an image using a probability distribution estimation algorithm.
However, Korean Patent No. 1384627 has a disadvantage in that a spatial distribution for color that can more precisely be segmented is not used in the segmentation of an object inside an image.
Therefore, in light of the recent explosive spread of 3D content, there is a need for a technology that is capable of precisely and automatically extracting an object of interest from an image.